Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 50
... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function ...
... density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function ...
Sivu 52
... density function for the unnormalized and untranslated variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are easily described . The contours of equal probability density are ...
... density function for the unnormalized and untranslated variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are easily described . The contours of equal probability density are ...
Sivu 59
... density for X , given M , is still given by Eq . ( 3.38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean u and covariance matrix Σ + K. That is , p ( X ) ~ N ...
... density for X , given M , is still given by Eq . ( 3.38 ) . The unconditional density for X can now be obtained by inspection , since Z and M are independent . Then X will have mean u and covariance matrix Σ + K. That is , p ( X ) ~ N ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |